Group each "graph dimension" in the legend of Plotly

đź“Š Plotly Python
1

I would like to add to the Plotly “standard legend” produced by plotly.express each of the “graph dimensions” grouping together all traces of such characteristics. As it is probably hard to understand what I want to do from the previous description, let me put an example. I have a code that produces a plot using the following line:

px.line(
	df,
	x = 'x values',
	y = 'y values',
	color = 'Device specs', # This is what I call "color dimension".
	symbol = 'Device', # This is what I call "symbol dimension".
	line_dash = 'Contact type', # This is what I call "line_dash dimension".
)

and the plot looks (for some specific data) like this:

enter image description here

I want to add below this legend one legend for each “dimension”, i.e. one legend for the color grouping all the traces of each color, one for the symbol and one for the line_dash, something like this:

enter image description here

and, if possible, such that if I click in e.g. contact=dot it toogles the visibility of all the dashed traces together.

Is this possible with Plotly Express?

2

After this answer in SO I coded this function

def add_grouped_legend(fig, data_frame, x, graph_dimensions):
	"""Create a grouped legend based on the example here https://stackoverflow.com/a/69829305/8849755
	- fig: The figure in which to add such grouped legend.
	- data_frame: The data frame from which to create the legend, in principle it should be the same that was plotted in `fig`.
	- graph_dimensions: A dictionary with the arguments such as `color`, `symbol`, `line_dash` passed to plotly.express functions you want to group, with the names of the columns in the data_frame."""
	param_list = [{'px': {dimension: dimension_value}, 'lg': {'legendgrouptitle_text': dimension_value}} for dimension, dimension_value in graph_dimensions.items()]
	legend_traces = []
	for param in param_list:
		this_dimension_trace = px.line(
			data_frame,
			x = x,
			y = [float('NaN') for i in range(len(data_frame))],
			**param["px"],
		).update_traces(
			**param["lg"],
			legendgroup = str(param["px"]),
		)
		if 'color' not in param['px']:
			this_dimension_trace.update_traces(
				marker = {'color': '#000000'},
				line = {'color': '#000000'},
			)
		legend_traces.append(this_dimension_trace)
	for t in legend_traces:
		fig.add_traces(t.data)

which does what I want. Full example below:

import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np

def add_grouped_legend(fig, data_frame, x, graph_dimensions):
	"""Create a grouped legend based on the example here https://stackoverflow.com/a/69829305/8849755
	- fig: The figure in which to add such grouped legend.
	- data_frame: The data frame from which to create the legend, in principle it should be the same that was plotted in `fig`.
	- graph_dimensions: A dictionary with the arguments such as `color`, `symbol`, `line_dash` passed to plotly.express functions you want to group, with the names of the columns in the data_frame."""
	param_list = [{'px': {dimension: dimension_value}, 'lg': {'legendgrouptitle_text': dimension_value}} for dimension, dimension_value in graph_dimensions.items()]
	legend_traces = []
	for param in param_list:
		this_dimension_trace = px.line(
			data_frame,
			x = x,
			y = [float('NaN') for i in range(len(data_frame))],
			**param["px"],
		).update_traces(
			**param["lg"],
			legendgroup = str(param["px"]),
		)
		if 'color' not in param['px']:
			this_dimension_trace.update_traces(
				marker = {'color': '#000000'},
				line = {'color': '#000000'},
			)
		legend_traces.append(this_dimension_trace)
	for t in legend_traces:
		fig.add_traces(t.data)

# Generate sample data set ---
SIZE = 10
df = pd.DataFrame(
    {
        "x values": np.tile(np.linspace(0, SIZE - 1, SIZE), SIZE),
        "y values": np.sort(np.random.uniform(1, 1000, SIZE ** 2)),
        "Device": np.concatenate(
            [np.full(SIZE, np.random.choice([52, 36, 34], 1)) for _ in range(SIZE)]
        ),
        "Contact type": np.concatenate(
            [np.full(SIZE, np.random.choice(["dot", "ring"], 1)) for _ in range(SIZE)]
        ),
        "Device specs": np.concatenate(
            [
                np.full(SIZE, np.random.choice(["laptop", "tablet", "console"], 1))
                for _ in range(SIZE)
            ]
        ),
    }
)
df.loc[df["x values"].eq(SIZE - 1), "y values"] = np.nan

# Do a regular plot ---
fig = px.line(
    data_frame = df,
    x = "x values",
    y = "y values",
    color = "Device",
    symbol = "Contact type",
    line_dash = 'Device specs',
)
# Add the grouped legend ---
add_grouped_legend(fig, df, 'x values', dict(color = "Device", symbol = "Contact type", line_dash = 'Device specs',))

fig.show()

and the result

image
image1856Ă—796 98.4 KB